FISCAL POLICY AND ETHNO-RACIAL INEQUALITY IN BOLIVIA,
BRAZIL, GUATEMALA AND URUGUAY
Nora Lustig
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Working Paper No. 22
January 2015
FISCAL POLICY AND ETHNO-RACIAL INEQUALITY IN
BOLIVIA, BRAZIL, GUATEMALA AND URUGUAY
Nora Lustig (Tulane University; nlustig@tulane.edu)*
CEQ-IDB Incidence of Taxes and Social Spending by Ethnicity and Race
CEQ Working Paper No. 22
JANUARY 2015
ABSTRACT
African descendants and indigenous peoples in Latin America face higher poverty rates and are
disproportionately represented among the poor. Per capita income of the white population can be sixty
percent higher to twice as high as the per capita income of the African descendant and indigenous populations.
Using comparable fiscal incidence analyses for Bolivia, Brazil, Guatemala and Uruguay, I analyze how much
poverty and inequality change after fiscal interventions. I also propose a set of indicators for measuring how
progressive and pro-indigenous or pro-African descendant government intervention is in ethno-racial
dimensions. Based on these indicators, I explore which elements of tax and transfer systems within each
country specifically contribute to narrowing or increasing existing ethno-racial gaps. The ratio of average per
capita incomes by ethnicity or race declines by at most one decimal point (Bolivia, Brazil and Uruguay) to
nothing (Guatemala). In Brazil and Uruguay, where there is a respectable decline in overall inequality, the
decline in inequality between different ethno-racial groups still does not decline significantly, changing by a
very small amount in Uruguay and actually increasing in Brazil.
JEL Codes: H22, I3, O1
Keywords: fiscal incidence, inequality, poverty, taxes, social spending, race, ethnicity, Latin America
* Nora Lustig is Samuel Z. Stone Professor of Latin American Economics, Tulane University and nonresident fellow at the
Center for Global Development and the Inter-American Dialogue.
1
ACKNOWLEDGEMENTS
This report is part of the CEQ-IADB “Incidence of Taxes and Social Spending by Ethnicity and Race,” a
joint project of the Gender and Diversity Division of the Inter-American Development Bank and the
Commitment to Equity project (CEQ). Led by Nora Lustig, the CEQ is a joint initiative of the Center for
Inter-American Policy and Research and the Department of Economics at Tulane University and the InterAmerican Dialogue (www.commitmentoequity.org). The fiscal incidence study for Bolivia was carried out by
Veronica Paz Arauco, George Gray-Molina, Wilson Jimenez and Ernesto Yañez; for Brazil by Sean Higgins
and Claudiney Pereira; for Guatemala by Maynor Cabrera and Hilcias E. Moran; and, for Uruguay by Marisa
Bucheli, Maximo Rossi and Florencia Amabile. Judith Morrison gave superb guidance throughout the project.
I am very grateful to Suzanne Duryea, Ariel Fiszbein, Miguel Jaramillo, Andrew Morrison, Marcos Robles and
participants of the November 21, 2013 and May 12, 2013 seminars at the Inter-American Development Bank
for their very useful comments and feedback on earlier drafts. Last but not least, I am also very grateful to
Ana Lucia Iturriza, Adam Ratzlaff and Eliana Rubiano for their excellent support in the coordination of the
project. I also wish to thank Adam Ratzlaff, Jacob Edelman, and David Roberts for their valuable research
assistantship.
2
TABLE OF CONTENTS
1. INTRODUCTION
2. INCIDENCE ANALYSIS BY ETHNIC AND RACIAL GROUPS: INDICATORS
Measuring the ethno-racial divide
Defining progressivity and pro-poorness in the ethno-racial dimension
3. MAIN RESULTS
Fiscal Policy, Inequality and Poverty in the Ethno-racial dimension
Fiscal Policy: Progressivity and Pro-poorness in the Ethno-racial dimension
a.
b.
c.
d.
Bolivia
Brazil
Guatemala
Uruguay
TABLES
Table 1 – Ethnic and Racial Inequality before Taxes and Transfers: Bolivia, Brazil, Guatemala and
Uruguay
Table 2 – Fiscal Policy Outcome Indicators and the Ethno-Racial Divide
Table 3 – Indicators of Progressivity, Pro-poorness and Horizontal Equity in the Ethno-Racial Divide
Table 4 – Ethno-racial Divide Before (Market Income) and After (Disposable Income) Taxes and
Transfers: Bolivia, Brazil, Guatemala and Uruguay
Table 5 – Differences in Probability of Being Poor by Ethnic and Racial Group by Income Concept
Table 6 –Progressivity of Taxes and Transfers in the Ethno-racial dimension
Table 7 – Probability of Escaping Poverty Through Direct Transfers
Table 8 – Horizontal Equity of Taxes and Transfers (Bolivia)
Table 9 – Horizontal Equity of Taxes and Transfers (Brazil)
Table 10 – Horizontal Equity of Taxes and Transfers (Guatemala)
Table 11 – Horizontal Equity of Taxes and Transfers (Uruguay)
FIGURES
Figure 1 – Incidence of Direct Transfers by Market Income Category and Ethno-Racial Group
Figure 2 – Growth Incidence Curves of Post-fiscal Income by Market Income Category and EthnoRacial Group
APPENDIX -- THE INCIDENCE OF SOCIAL SPENDING AND TAXES: METHODOLOGICAL HIGHLIGHTS
REFERENCES
3
1. INTRODUCTION
Ethnic and racial differences in human capital and earnings are one of the key determinants of inequality in
Latin America. African descendants and indigenous peoples have systematically higher poverty rates and are
disproportionately represented among the poor. These groups have lower education levels, lower earnings
and access to services, and are more likely to work in low-productivity jobs in the informal sector (de Ferranti
et al., 2004; Hall and Patrinos, 2006; Ñopo, 2012).
Given these facts, the extent to which governments use their power to tax and spend to reduce the welfare
gaps between ethnic and racial groups is of great importance.1 The importance of reducing the ethno-racial
divide arises both from what this divide means ethically as well as its causes and consequences. Ethnic and
racial groups are what philosophers call “morally relevant” groups and, as such, sharp inequalities between
them are not ethically acceptable. This is exacerbated by the fact that today’s ethnic and racial inequalities are
often the product of morally condemnable societal actions such as discrimination in the present and
subjugation of indigenous groups and slavery in the past. Finally, ethnic and racial inequalities are found to be
associated with lower overall development and growth (Alesina et al., 2012); thus, addressing ethno-racial
inequalities may have the additional benefit of generating higher welfare levels for everyone.
Using comparable fiscal incidence analyses for Bolivia, Brazil, Guatemala and Uruguay,2 this paper analyzes
the effect of taxes and social spending on ethnic and racial inequality. Except for Uruguay, where Afrodescendants and indigenous groups represent only 4.4 percent of the total population, the share of the
indigenous or Afro-descendant population in these countries is large: representing 54.2 percent of the
population in Bolivia, 50.8 percent in Brazil and 40.7 percent in Guatemala.3
Ethnic and racial inequality in these four countries is high (Table 1). Per capita pre-fiscal income of the white
population is between sixty percent and two times higher than the Afro-descendants or indigenous
population’s income. Inequality between ethnic or racial groups accounts for between 1 percent of total
inequality in Uruguay to 9.1 percent in Brazil. The indigenous and Afro-descendants populations represent a
considerably larger share of the poor than they do of the total population. The probability of being poor
(measured by the headcount ratio using the international poverty line of $2.50 in purchasing power parity
dollars per day) is between two and three times higher for indigenous and Afro-descendants than whites.
Average educational attainment levels are roughly between two and three years lower for Afro-descendants
In this paper, fiscal policy, fiscal interventions and taxes and transfers policy are used interchangeably.
These studies were produced under the Commitment to Equity-Inter-American Development Bank (CEQ-IADB) project
“Incidence of Taxes and Social Spending by Ethnicity and Race.” The fiscal incidence study for Bolivia was carried out by Veronica
Paz Arauco, George Gray-Molina, Wilson Jimenez and Ernesto Yañez (2013); for Brazil by Sean Higgins and Claudiney Pereira
(2013); for Guatemala by Maynor Cabrera and Hilcias E. Moran (2014); and, for Uruguay by Marisa Bucheli, Maximo Rossi and
Florencia Amabile (2014). The household surveys used for the analyses are: Bolivia (Encuesta de Hogares, 2009), Brazil (Pesquisa
de Orçamentos Familiares, 2009), Guatemala (Encuesta Nacional de Ingresos y Gastos de las Familias, 2009-2010) and Uruguay
(Encuesta Continua de Hogares, 2009).
3 The ethnicity and race of each individual in the household surveys was determined by self-identification. For details see the
country-by-country descriptions available upon request. In Brazil, although data is available for white, Asian, blacks (pretos), pardos
(literally, brown), and indigenous, for the purpose of this analysis the non-white population refers to Afro-Brazilians which is the
combination of pretos and pardos, with pardos representing the majority of the group (43 percent of total population). Disaggregated
data for the different ethno-racial groups is available upon request.
1
2
4
or indigenous populations in all four countries. As we shall see below, although taxes and transfers reduce
these gaps, the change is very small.
2. INCIDENCE ANALYSIS BY ETHNIC AND RACIAL GROUPS: INDICATORS
i Measuring the ethno-racial divide
A fiscal incidence analysis designed to assess how governments reduce the welfare gap between ethnic and
racial groups needs to include indicators that can capture how inequities across these groups change with fiscal
interventions. A necessary first step is to select indicators to measure the ethno-racial divide.4 Here I propose
to use the following.5
Income Gap: The ethno-racial gap can be measured by simply taking the ratio of per capita incomes between
different groups.
Contribution to Overall Inequality: The contribution of the ethno-racial income gap to overall inequality can be
estimated using a standard decomposable inequality index such as the Theil index.
Inequality of Opportunity: A society with high ethno-racial equity should feature fairly equal opportunities across
ethnic and racial groups. To assess the extent to which fiscal policy equalizes opportunities, following the
ideas originally set out by Roemer (1998) and their application by Ferreira et al. (2012), I propose to use an
indicator that can track the extent to which taxes and transfers reduce the inequality associated with
circumstances. Circumstances are pre-determined factors that are not dependent on an individual’s effort,
such as ethnicity and race, gender, place of birth, and parents’ education or parents’ income. In these national
surveys, information on parents or place of birth is not available. Thus, for our purposes, circumstances
include race or ethnic group, gender and location (rural or urban).6
The ethno-racial divide exists well beyond the income or public services space but for the purposes of a fiscal incidence analysis
we focus on the latter.
5 In addition to the present list, one may want to consider other outcome variables that are not necessarily present in the income or
public services space. For example, one may want to assess access to urban infrastructure (water and sanitation, street lighting, and
so on) or the extent to which the fiscal system exacerbates or reduces occupational segregation or discrimination. These are not
included in this paper.
6 Once each individual’s circumstances set has been identified, the mean income of each circumstances set (i.e., the mean income
of all individuals in that circumstances set) is calculated for the “pre fisc” and the “post fisc” income. Let 𝑠 indicate the income of
each individual i, which in the smoothed distribution equals the mean income for income concept 𝑗 (where the latter can be before
taxes and transfers or after taxes and/or transfers, see Diagram 1 in Appendix) of everyone in individual 𝑖’s circumstances set. Each
individual is attributed the mean income of their circumstances set, and this income distribution is called the smoothed income
distribution. Inequality is then measured over the smoothed income distribution for each income concept associated with taxes and
transfers. Here the mean log deviation was used, which gives the measure of inequality of opportunity (in levels) by income concept.
The mean log deviation of the smoothed distribution (for income concept 𝑗) is calculated as
1
𝜇
ln 𝑛
𝑠
4
where 𝜇 is the mean income of the population for income concept 𝑗 (either the original or smoothed distribution can be used to
calculate 𝜇 since they have the same mean by definition), and 𝑠 is defined above.
5
Poverty: An indication of ethnic and racial inequity is the extent to which the probability of being poor differs
across ethno-racial groups. This can be measured with the incidence or headcount ratios for different ethnic
and racial groups.
The above indicators can be estimated for different income concepts that take into account fiscal
interventions.7 In particular, one would like to compare the size of these indicators measured with income
before taxes and transfers (or, market income) with their size using disposable income, where the latter equals
market income less direct taxes (personal income taxes and contributions to social security) plus direct
transfers (cash and near cash transfers such as food transfers and school uniforms) (Table 2). As we shall see
below, we may also want to measure poverty after taking into account the effects of consumption taxes and
subsidies (indirect net taxes).
ii Defining progressivity and pro-poorness in the ethno-racial dimension
The above indicators of the fiscal system are outcomes of the characteristics of the fiscal system in terms of
progressivity and pro-poorness. These concepts are fairly developed in the literature on fiscal incidence in the
income space.8 Here I propose an adaptation of these concepts to the ethno-racial dimension (Table 3).
Progressivity: In order to measure progressivity between ethnic and racial groups, I propose to use an approach
analogous to that used for analyzing taxes and transfers in the income space. The ethnic or racial groups shall
be ranked based on their per capita market income. A tax will be defined as progressive (regressive) in the
ethno-racial sense if the share paid by the ethnic or racial group with the highest per capita income is higher
(lower) than their market income share. A transfer will be defined as progressive (regressive) if the share
received by the ethnic or racial group with the lowest per capita income is higher (lower) than their market
income share. A transfer will be defined as progressive in absolute terms if the share received by the ethnic or
racial group with the lowest per capita income is higher than their population share.
Pro-disadvantaged Group: I propose to use the following measures to assess how pro-indigenous or pro-Afrodescendant the fiscal system is. Given the large difference in market income poverty rates between whites and
indigenous peoples and Afro-descendants, a pro-disadvantaged-group fiscal system should feature a higher
probability of escaping poverty after taxes and transfers for the ethnic or racial groups with higher incidences
of poverty.9 This would also imply that net transfers should be higher for the poor in the ethnic or racial group
with the highest incidence of poverty.
The directional mobility literature provides a useful framework to measure the transfers-induced probability
of escaping poverty.10 In particular, the probability of escaping poverty can be estimated using a Markovian
transition matrix that Higgins and Lustig (2013) called a “fiscal mobility matrix” (FMM). Fiscal mobility is the
See the definition of the income concepts in Diagram 1 in the methodological appendix at the end.
On progressivity see, for example, Lambert (1988) and Duclos (1997, 2008).
9 This probability is calculated using the “hazard rates” as in Amabile et al. (2014). However, in contrast to these authors, I do not
believe that racial equity entails having the same hazard rates for all the groups. Thus, the probability of escaping poverty means the
probability of crossing this threshold with transfers. Recall that for all the poverty measures here I use the international poverty line
of $2.50 in purchasing power parity dollars per day.
10 Directional mobility is a subcategory of the “mobility as movement” definition (as opposed to the time independence definition).
See Fields (2008) for a survey of the income mobility literature.
7
8
6
directional movement between the before and after taxes and transfers situations among 𝑘 pre-defined
income categories. It can be represented by the 𝑘 × 𝑘 transition matrix 𝑃, where the 𝑖𝑗th element of 𝑃,
denoted 𝑝 , can be interpreted as the probability of moving to income group 𝑗 after taxes and transfers for
individuals who were in income group 𝑖 before taxes and transfers. Hence, 𝑃 is a row stochastic matrix with
∑ 𝑝 = 1 for all 𝑖 ∈ {1, … , 𝑘}.11 Define 𝒛 as a vector of poverty lines between 𝑧 (the lowest reasonable
poverty line) and 𝑧̅ (the highest reasonable poverty line). In other words, 𝒛 is an ordered vector whose
component values define tranches of income ranges which demarcate varying degrees of poverty severity.12
These poverty lines will determine a subset 𝑟 of the 𝑘 income categories (𝑟 < 𝑘) for which 𝑝 denotes the
probability of moving out of poverty (poverty) after taxes and transfers, for individuals who were poor before
taxes and transfers. The probability of escaping poverty is estimated by the following: ∑ ∑ : 𝑝 > 0 .
In the estimates presented here, I set r = 1, with r being the $2.50 per day in purchasing power parity dollars
international poverty line. The number of k income categories is set equal to two: poor and non-poor. Thus,
the transition matrix or FMM is a two by two.13
Horizontal Equity: Note that a “pro-disadvantaged group” criterion will often run in contradiction to the
principle of horizontal equity. Horizontal equity entails that the fiscal system should treat pre-fiscal equals equally
(Feldstein, 1976).14 Under such a principle, one could argue that the poor should be treated the same regardless
of their race or ethnicity. If one embraces this principle, the share of net transfers received by equally poor
individuals of any racial or ethnic groups should be equal to their share of the poor population. That is, the
average per capita net transfers going to the poor should be the same regardless of their race or ethnicity. In
general, this will not result in equalizing the probability of escaping poverty across racial and ethnic groups.
As with gender, inequities in the fiscal system between ethnic and racial groups can arise due to explicit
provisions in the tax and transfers system or can be implicit. The latter occur when taxes and transfers have a
differential impact on ethnic and racial groups due to, for example, the geographic distribution of these groups
(e.g., rural versus urban), employment status (e.g., informal versus formal sector) or consumption patterns
(e.g., level of taxes on specific goods or location of purchase). Absence of ethno-racial inequity in the statutory
design of a tax or a transfer can give a false sense of comfort since the inequities may result from implicit
biases.
Mobility matrices were used to compare pre- and post-tax and transfer income distributions in Lustig et al. (2011).
We are grateful to Peter Lambert for suggesting this interpretation of the vector of poverty lines.
13 Note that the probability of becoming poorer with the fiscal system, especially after consumption taxes can be positive as well.
In such circumstances there is downward mobility among the poor. If ∑ ∑ : 𝑝 > 0, then there is downward mobility among
∑ : 𝑝 > 0 then there is downward mobility of some non-poor into poverty. Higgins and Lustig (2013) call
the poor. If ∑
this “fiscal impoverishment” and axiomatically develop a measure.
14 Given limited resources if one wants to give priority to equalizing opportunities or outcomes across ethno-racial groups, this may
mean to transfer all or most the resources to the poor in the disadvantaged group and none or almost none to the equally poor in
the non-disadvantaged group.
11
12
7
3. MAIN RESULTS
i Fiscal Policy, Inequality and Poverty in the Ethno-racial dimension
What is the impact of direct taxes and direct transfers on ethnic and racial inequality? Using the indicators
described above, Table 4 reveals that the answer is a simple “not much.” Although all the indicators move in
the right direction, the order of magnitude of the change is, with the exception of Uruguay, quite small.
The ratio of average per capita incomes by ethnicity or race declines by one decimal point (Bolivia, Brazil and
Uruguay) to nothing (Guatemala). In Brazil and Uruguay, where there is a respectable decline in overall
inequality, the decline in inequality between different ethno-racial groups still does not decline significantly,
changing by a very small amount in Uruguay and actually increasing in Brazil. The case of Brazil indicates that
inequality within each ethno-racial group (intra-racial inequality) is falling (due to the effect of taxes and
transfers) at a faster rate than the inequality evident between different races (inter-racial inequality). Inequality
of opportunity also declines by a relatively small amount, this occurs even though all three circumstances are
considered simultaneously.
Although the difference in the probability of being poor after taxes and transfers is less than before taxes and
transfers,15 the difference in headcount ratios by ethnic group and race remain very large, with the exception
of Uruguay. More importantly, when one adds the effect of consumption taxes, the gap in the headcount ratio
increases above that for market income in Brazil and remains unchanged in Bolivia (Table 5). In the cases of
Guatemala and Uruguay, while the narrowing of the gap is somewhat offset by consumption taxes, the
difference is still smaller than for market income. That is, when the combined effect of direct and indirect (net
of subsidies) taxes and direct transfers is considered, fiscal interventions reduce the differences in the
probability of being poor between ethnic and racial groups in Guatemala and, especially, Uruguay, while in
Brazil they increase the ethno-racial gap and in Bolivia there is little to no change.
ii Fiscal Policy: Progressivity and Pro-disadvantage Group in the Ethno-racial dimension
As seen above, fiscal interventions have little impact on the indicators we selected to measure the ethno-racial
divide. In fact, when (net) indirect taxes are added, the differences in the probability of being poor move in
the wrong direction (Brazil) or not at all (Bolivia). Are there specific characteristics of the fiscal system that
may be associated with these rather disappointing outcomes?
a. Bolivia
Although transfers are progressive in absolute terms in the ethno-racial dimension (i.e., shares are higher for
indigenous peoples than their population share), they are not progressive enough. For example, the share of
non-contributory pensions, CCTs and other direct transfers going to the indigenous population living in
poverty is lower than their share in the total poor population.
Recall that for all the poverty measures here I use the international poverty line of $2.50 in purchasing power parity dollars per
day.
15
8
In Table 7 we can observe that while the probability of escaping poverty through direct transfers is higher for
indigenous peoples than the non-indigenous population, it is only slightly so.16 In order to narrow the gap in
the headcount ratios, the difference would have to be much higher. For example, if the goal were to equalize
the incidence of disposable income poverty between the disadvantaged group to the market income poverty
rate of the non-disadvantaged group, the probability of escaping poverty for Bolivia’s indigenous population
would have to be 63.5 percent instead of just 10.4 percent. This means that transfers to this group would have
to be significantly higher than for the non-indigenous poor. In Figure 1, we can see that at present, they are
quite similar for the poor in both ethnic groups.
Could Bolivia do better in terms of reducing the difference in indigenous and non-indigenous headcount
ratios with the same amount of direct transfers? From Paz-Arauco et al. (2014), we know that direct transfers
in Bolivia are quasi-universal which means that a significant portion of transfers are received by the non-poor.
With better targeting, these resources could be used to expand the coverage and/or average size of transfers
for indigenous groups so that the probability of escaping poverty for these groups would be higher. It should
be noted, however, that cash transfer coverage for the non-indigenous poor is considerably lower, so reducing
the gap in the probability of being poor between the two ethnic groups would be at the cost of higher
horizontal inequity within the poor. That is, the indigenous group already features higher coverage and higher
per capita transfers in beneficiary households than the non-indigenous poor. The required change would make
this difference more pronounced.
On the tax side, the situation is significantly worse. As we see in Table 6, consumption taxes are regressive in
the ethno-racial dimension: the indigenous population pays a higher share of these taxes than their share in
total market income. 17 It remains to be seen which factors are behind this result. For example, it would be
interesting to assess the extent to which consumption patterns, both in terms of goods purchased and location
of purchase, explain the differences in taxation rates.18
b. Brazil
Direct transfers are progressive in the ethno-racial dimension, but not progressive enough. This is because
the very progressive CCT, Bolsa Familia, is partially offset by the Special Circumstances Pensions, which is
neutral in the ethno-racial dimension (that is, the share of the transfer is practically the same as the share of
market income), and by scholarships, which are regressive.19 In fact, as Figure 1 shows, the poorest segment
of the white population receives nearly twice as much in direct transfers as equally poor Afro-Brazilians.20
This is primarily due to Special Circumstances Pensions.
The Special Circumstances Pension includes social protection programs against illness, disability, widowhood,
orphanhood and other adverse shocks. Although these are paid through the formal social security system in
Recall that for all the poverty measures here I use the international poverty line of $2.50 in purchasing power parity dollars per
day. Thus, the probability of escaping poverty means the probability of crossing this threshold with transfers.
17 As Paz-Arauco et al., op. cit., explain, Bolivia —for all practical purposes—does not have personal income taxes.
18 See Higgins and Lustig (2013) for a similar analysis for Brazil.
19 For a description of this program, see Higgins and Pereira (2014).
20 The national survey used for Brazil (Pesquisa Nacional por Amostra de Domicílios) allows for the disaggregation of race into five
groups; white, black (preto), brown (pardo), Asian, and indigenous. For the purpose of this paper, the analysis compares the white
population to the Afro-Brazilian population. The Afro-Brazilian population includes both pretos and pardos.
16
9
which beneficiaries need to be enrolled, individuals can be eligible to receive benefits even if they have not
made contributions to the system. Coverage by the special circumstances pension system is higher for the
white population, especially among the poorest groups. The per capita benefit is also higher for the white
population as a whole in all income groups, including the poorest. The white population also benefits more
from scholarships primarily because the average per capita transfer is higher. Except for Bolsa Familia, the
poorer groups among the white population not only have higher per capita transfers but also higher coverage
rates.
The above probably explains why the probability of escaping poverty through transfers is higher for Brazil’s
white population than for Afro-descendants (Table 7). If the goal were to equalize the disposable income
poverty of Afro-Brazilians to the market income poverty rate of the white population, the probability of
escaping poverty for the Afro-Brazilian population would have to be 62.2 percent instead of 24.3 percent.
This means that transfers to this group would have to be significantly higher than for the white poor, which
is the opposite of what the fiscal incidence analysis shows is happening.
Brazil could improve the redistributive power of its cash transfers both in the income (Higgins and Pereira,
2014) and, especially, in the ethno-racial dimension, if the benefits of the Special Circumstances Pensions were
expanded to rural areas and to pardo, indigenous and black groups.
Although consumption taxes are neutral in the ethno-racial dimension, the net effect regarding the number
of individuals pushed below the poverty line as a result of consumption taxes must have offset the number
of people who were pushed out of poverty among the whites by more than among the Afro-descendants.
This explains the increase in the difference between headcount ratios with post-fiscal income shown in Table
5. It would be interesting to check whether there are identifiable differences in the consumption patterns
between racial groups that may explain what we found in terms of the effects of consumption taxes.
c. Guatemala
As seen in Table 4, transfers-induced poverty reduction in Guatemala is small for both the non-indigenous
and indigenous populations. As in the cases of Bolivia and Brazil, transfers are progressive in the ethno-racial
dimension but not enough. The progressivity of the CCT program is partially offset by the regressivity of
noncontributory pensions and other direct transfers. 21 In contrast to Brazil, however, the incidence of
transfers is higher for the indigenous population and the difference is larger for the income category with less
than U$2.50 ppp per day (Figure 1).
In Table 7 we can observe that the probability of escaping poverty through direct transfers is higher for the
indigenous than the non-indigenous population, although both are painstakingly small.22 In order to narrow
the gap in headcount ratios, the difference would have to be much higher. If like in the cases of Bolivia and
Brazil the goal were to equalize the disposable income poverty of the disadvantaged group to the market
income poverty of the non-disadvantaged group, for example, the probability of escaping poverty for the
For a description of transfer programs, see Cabrera et al. (2014).
Recall that for all the poverty measures here I use the international poverty line of $2.50 in purchasing power parity dollars per
day. Thus, the probability of escaping poverty means the probability of crossing this threshold with transfers.
21
22
10
indigenous population would have to be 53.3 percent instead of 4.7 percent. This means that transfers to the
indigenous poor, relative to the non-indigenous poor, would have to be even higher than they are now.
Direct taxes and consumption taxes are progressive in the ethno-racial dimension. Overall, taxes and cash
transfers in Guatemala are “pro-indigenous” but the amount that is redistributed in cash to either ethnic group
is very small and some of the individual transfers still benefit the non-indigenous more than the indigenous
population.
d. Uruguay
Uruguay narrows the difference in the headcount ratio between the non-Afro-descendant and Afrodescendant populations more than any of the other countries examined here. In fact, the ex-post disposable
income headcount ratios are quite low and similar. This is no coincidence. Not only does Uruguay have higher
per capita incomes, lower overall inequality and considerably lower poverty rates than the other three
countries, but Uruguay’s policies are also significantly more progressive in the ethno-racial dimension. In
Table 6 one can observe that direct taxes are progressive and, above all, that direct transfers as a whole, as
well as each transfer individually, are progressive in absolute terms in the ethno-racial dimension: that is, that
per capita transfers are higher for groups which experience a higher incidence of poverty.
It is also the only country for which the actual transfers-induced probability of escaping poverty is higher than
what would be required to equalize the poverty headcount ratio between the Afro-descendants disposable
income and the non-Afro-descendants market income poverty headcount ratio. The probability of escaping
poverty for the Afro-descendants is 70.3 percent while what would be required is 61.3 percent.
The only category that is not progressive in the ethno-racial dimension is consumption taxes, which turn out
to be slightly regressive or neutral. Again, it would be interesting to determine if this is associated with
differences in consumption patterns between non-Afro-descendant and Afro-descendant populations.
11
TABLE 1 – ETHNIC AND RACIAL INEQUALITY BEFORE TAXES AND TRANSFERS: BOLIVIA, BRAZIL,
GUATEMALA AND URUGUAY
Indicator
Bolivia
(2009)
1.6
Brazil
(2009)
2.1
Guatemala
(2009/10)
2.1
Uruguay
(2009)
1.8
.497
4.9
.674
9.1
.692
8.5
.456
1
54.2
50.8
40.7
4.4
Nonwhite Population in Poor** (in %)
76.5
72.8
59.5
9.5
Headcount Ratio of White
Population** (in %)
Headcount Ratio of Nonwhite
Population**(in %)
Poverty Gap of White Population**
(in %)
Poverty Gap of Nonwhite
Population**(in %)
Poverty Gap Squared of White
Population** (in %)
Poverty Gap Squared of Nonwhite
Population**(in %)
Average Years of Schooling of White
Population (+25 yrs old)
10.1
8.2
24.5
4.8
27.7
21.7
52.5
12.4
4.1
3.3
8.5
1.7
14.7
9.3
19.2
4.7
2.4
1.9
4.1
.9
9.7
5.5
9.5
2.5
9.7
8.2
5.8
8.8
Average Years of Schooling of
Nonwhite Population (+25 yrs old)
7
6.3
2.3
6.8
White/Nonwhite Average Per Capita
Market Income*
Theil Index
Contribution of Between Race
Inequality*** (in %)
Nonwhite Population in Total* (in %)
Source: Author’s based on Bolivia (2009): Paz-Arauco et al., 2013 CEQ-IDB; Brazil (2009): Higgins and Pereira, 2013
CEQ-IDB; Guatemala (2010/2011): Cabrera and Moran, 2013 CEQ-IDB; Uruguay (2009): Bucheli, Rossi and Amabile,
2013 CEQ-IDB.
Note: Household surveys used for the analyses are: Bolivia (Encuesta de Hogares, 2009), Brazil (Pesquisa de
Orçamentos Familiares, 2009), Guatemala (Encuesta Nacional de Ingresos y Gastos de las Familias, 2009-2010) and
Uruguay (Encuesta Continua de Hogares, 2009). All the presented above measures use pre-fisc or market income,
defined as gross wages and salaries, income from capital, private transfers and contributory pensions; it includes selfconsumption (except for Bolivia) and imputed rent for owner’s occupied housing. *The nonwhite population for Bolivia
and Guatemala refer to the indigenous population; in the case of Brazil, to the Afro-Brazilian (pardo and preto)
population; and, in the case of Uruguay, to Afro-descendants. **Poverty is measured for per capita market income with
the international poverty line of US$2.50 ppp per day. ***This corresponds to the “between” component of a standard
decomposition of the Theil index.
12
TABLE 2 – FISCAL POLICY OUTCOME INDICATORS AND THE ETHNO-RACIAL DIVIDE
Outcome
Indicator
White/nonwhite average per capita market vs. disposable income
Inequality
Decomposable inequality measure (e.g., Theil index) for market
income vs. disposable income
Contribution of between race inequality to overall inequality for
market income vs. disposable income
Inequality of
Opportunity
Smoothed inequality measure over circumstances, including
ethnicity or race as one of them (e.g., Mean Log Deviation) for
market income vs. disposable income
Poverty
Headcount Ratio, Poverty Gap Index and Squared Poverty Gap
Index for the White Population Minus Headcount ratio for the
Non-white Population for market income vs. disposable income
and post-fiscal income
TABLE 3 – INDICATORS OF PROGRESSIVITY, PRO-DISADVANTAGED GROUP AND HORIZONTAL
EQUITY IN THE ETHNO-RACIAL DIVIDE
Dimension of Fiscal
System
Indicator
Progressivity
Share of taxes (transfers) paid (received) by each ethnic or racial
group compared to the respective shares of market income and
population
Pro-disadvantaged
group
Probability of escaping poverty (impoverishment) by ethnic or
racial group
Horizontal equity
among the poor
The share of taxes and transfers is the same as the population
shares by race and ethnicity within the poor
13
TABLE 4 – ETHNO-RACIAL DIVIDE BEFORE (MARKET INCOME) AND AFTER (DISPOSABLE INCOME)
TAXES AND TRANSFERS: BOLIVIA, BRAZIL, GUATEMALA AND URUGUAY
Indicator
Bolivia
Brazil
Guatemala
Uruguay
Market
Income
Disposable
Income
Market
Income
Disposable
Income
Market
Income
Disposable
Income
Market
Income
Disposable
Income
White/nonwhite
average per capita
income*
1.5
1.5
2.1
2
2.1
2.1
1.8
1.7
Theil Index
.497
.478
.674
.588
.692
.682
.456
.389
Contribution of
between race
inequality*** (in
%)
4.9
4.8
9.1
9.2
8.5
8.4
.96
.96
Inequality of
opportunity****
0.092
0.082
0.096
0.083
0.197
0.195
0.013
0.011
Headcount ratio
of white
population** (in
%)
10.1
9.1
8.2
5.6
24.5
24
4.8
1.4
Headcount ratio
of nonwhite
population**(in
%)
27.7
24.8
21.7
16.4
52.5
50.1
12.4
3.7
Source: Author’s based on Bolivia (2009): Paz-Arauco et al., 2013 CEQ-IDB; Brazil (2009): Higgins and Pereira, 2013
CEQ-IDB; Guatemala (2010/2011): Cabrera and Moran, 2013 CEQ-IDB; Uruguay (2009): Bucheli, Rossi and Amabile,
2013 CEQ-IDB.
Note: Market income is defined as gross wages and salaries, income from capital, private transfers and contributory
pensions; it includes self-consumption (except for Bolivia) and imputed rent for owner’s occupied housing. Disposable
income equals market income minus personal income taxes and (non-pension) contributions to social security plus
direct transfers (cash and near cash). *The nonwhite population for Bolivia and Guatemala refer to the indigenous
population; in the case of Brazil, to the Afro-Brazilian (pardo and preto) population; and, in the case of Uruguay, to the
Afro-descendants. **Poverty is measured with the international poverty line of US$2.50 ppp per day. ***This
corresponds to the “between” component of a standard decomposition of the Theil index. ****Mean log deviation of
smoothed distribution with gender of head, location (rural or urban), and race/ethnicity as circumstances.
14
TABLE 5 – DIFFERENCES IN PROBABILITY OF BEING POOR BY ETHNIC AND RACIAL GROUP BY
INCOME CONCEPT
Headcount Ratio for the Market
Non-white
Population Income
Minus Headcount ratio for
White
Population
in
Percentage Points
Disposable
Income
Post-fiscal
Income
Bolivia
17.6
15.7
16.9
Brazil
13.5
10.8
14
Guatemala
28
26.1
27.1
Uruguay
7.6
2.3
3.1
Source: Author’s based on Bolivia (2009): Paz-Arauco et al., 2013 CEQ-IDB; Brazil (2009): Higgins and Pereira, 2013
CEQ-IDB; Guatemala (2010/2011): Cabrera and Moran, 2013 CEQ-IDB; Uruguay (2009): Bucheli, Rossi and Amabile,
2013 CEQ-IDB.
Note: The probability of being poor is measured as the headcount ratio. Market income is defined as gross wages and
salaries, income from capital, private transfers and contributory pensions; it includes self-consumption (except for
Bolivia) and imputed rent for owner’s occupied housing. Disposable income equals market income minus personal
income taxes and (non-pension) contributions to social security plus direct transfers (cash and near cash). Post-fiscal
income is defined as disposable income minus net indirect taxes. *The nonwhite population for Bolivia and Guatemala
refer to the indigenous population; in the case of Brazil, to the Afro-Brazilian (pardo and preto) population; and, in the
case of Uruguay, to the Afro-descendants. **Poverty is measured with the international poverty line of US$2.50 ppp
per day.
15
TABLE 6 –PROGRESSIVITY OF TAXES AND TRANSFERS IN THE ETHNO-RACIAL DIMENSION
(Shares in percent)
Bolivia
POPULATION
MARKET INCOME
All Direct Transfers
Non-contributory Pensions
Flagship CCT
Other Direct Transfers
Indirect Subsidies
Indirect Taxes
Net Indirect Taxes
Guatemala
POPULATION
MARKET INCOME
All Direct
CCT
Non-Contributory Pensions
Other Direct Transfers
Indirect Subsidies
Indirect Taxes
Net Indirect Taxes
NON INDIGENOUS
% nat'l
45.80%
56.61%
39.36%
38.71%
38.75%
43.69%
57.40%
56.28%
56.17%
INDIGENOUS
% nat'l
54.17%
43.28%
60.53%
61.14%
61.25%
56.31%
42.54%
43.51%
43.61%
NON INDIGENOUS
% nat'l
59.29%
75.61%
35.29%
24.02%
62.03%
75.64%
77.89%
78.01%
78.03%
INDIGENOUS
% nat'l
40.71%
24.39%
64.71%
75.98%
37.97%
24.36%
22.11%
21.99%
21.97%
Brazil
POPULATION
MARKET INCOME
Direct Taxes
DirectTaxes&Contributions
All Direct Transfers
CCT
Scholarships
Special Circums Pensions
Unemployment Benefits
Other Direct Transfers
Indirect Taxes
Net Indirect Taxes
Uruguay
POPULATION
MARKET INCOME
Direct Taxes
All Direct Transfers
CCT
Non-contributory pension
Other Direct Transfers
Indirect Taxes
Net Indirect Taxes
WHITE
% nat'l
48.0%
64.9%
71.4%
70.5%
56.9%
26.6%
72.2%
64.5%
50.5%
51.9%
63.6%
63.7%
AFRODESCENDANTS (Preto + Pardo)
% nat'l
50.8%
33.4%
26.8%
27.7%
42.2%
72.3%
27.4%
34.7%
48.9%
47.6%
34.8%
34.7%
WHITE
% nat'l
95.5%
97.2%
97.6%
92.8%
91.9%
92.6%
93.3%
97.2%
97.2%
AFRODESCENDANT
% nat'l
3.4%
1.9%
1.7%
5.8%
6.7%
6.1%
5.3%
1.9%
1.9%
INDIGENOUS
% nat'l
1.0%
0.7%
0.6%
1.3%
1.4%
1.3%
1.3%
0.8%
0.8%
Source: Author’s based on Bolivia (2009): Paz-Arauco et al., 2013 CEQ-IDB; Brazil (2009): Higgins and Pereira, 2013
CEQ-IDB; Guatemala (2010/2011): Cabrera and Moran, 2013 CEQ-IDB; Uruguay (2009): Bucheli, Rossi and Amabile,
2013 CEQ-IDB.
TABLE 7 – PROBABILITY OF ESCAPING POVERTY THROUGH DIRECT TRANSFERS (IN PERCENT)
Bolivia
Brazil
Guatemala
Uruguay
National
10.4
26.1
3.7
71.5
White
10.1
31.2
2.2
71.5
Non-White
10.4
24.3
4.7
70.3
Source: Author’s based on Bolivia (2009): Paz-Arauco et al., 2013 CEQ-IDB; Brazil (2009): Higgins and Pereira, 2013
CEQ-IDB; Guatemala (2010/2011): Cabrera and Moran, 2013 CEQ-IDB; Uruguay (2009): Bucheli, Rossi and Amabile,
2013 CEQ-IDB.
Note: Poverty is measured with the international poverty line of US$2.50 ppp per day.
16
TABLE 8 – HORIZONTAL EQUITY OF TAXES AND TRANSFERS (BOLIVIA)
NON INDIGENOUS
INDIGENOUS
y<=1.25
% nat'l
15.43%
% nat'l
84.57%
1.25<y<=2.50
31.91%
68.09%
2.50<y<=4
40.08%
59.92%
y<=1.25
15.95%
84.05%
1.25<y<=2.50
32.51%
67.49%
2.50<y<=4
40.05%
59.95%
y<=1.25
15.87%
84.13%
1.25<y<=2.50
20.88%
79.12%
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
33.70%
14.25%
18.16%
29.69%
9.89%
31.95%
39.36%
33.74%
66.30%
85.75%
81.84%
70.31%
90.09%
68.05%
60.64%
66.26%
1.25<y<=2.50
22.77%
77.23%
2.50<y<=4
40.05%
59.95%
Bolivia
POPULATION
MARKET INCOME
All Direct Transfers
Non-contributory Pensions
Flagship CCT
Other Direct Transfers
y<=1.25
54.81%
45.19%
Indirect Subsidies
1.25<y<=2.50
50.69%
49.31%
Indirect Taxes
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
45.11%
25.17%
37.28%
41.41%
23.23%
54.87%
74.83%
62.72%
58.59%
76.77%
Net Indirect Taxes
1.25<y<=2.50
35.91%
64.09%
2.50<y<=4
40.98%
59.02%
Source: Bolivia (2009): Paz-Arauco et al., 2013 CEQ-IDB.
17
TABLE 9 – HORIZONTAL EQUITY OF TAXES AND TRANSFERS (BRAZIL)
Brazil
POPULATION
MARKET INCOME
Direct Taxes
Direct Taxes &
Contributions
All Direct Transfers
CCT
Scholarships
Special Circumstances
Pensions
Unemployment
Benefits
Other Direct
Transfers
Indirect Taxes
Net Indirect Taxes
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
WHITE
AFRODESCENDANTS (Preto + Pardo)
% nat'l
23.6%
27.6%
33.0%
23.5%
27.6%
33.0%
44.5%
33.0%
41.0%
44.1%
32.0%
39.5%
33.2%
36.8%
39.4%
21.3%
24.4%
26.7%
94.7%
64.2%
35.7%
40.8%
49.6%
51.8%
24.0%
28.5%
26.8%
11.4%
28.6%
32.9%
28.8%
30.4%
34.2%
30.1%
31.0%
34.4%
% nat'l
75.2%
71.4%
65.6%
75.5%
71.4%
65.6%
53.3%
65.9%
57.8%
53.7%
66.8%
59.3%
65.7%
62.2%
59.7%
77.5%
74.6%
72.1%
5.2%
35.3%
64.0%
57.9%
49.6%
47.5%
76.0%
70.9%
72.3%
88.3%
71.2%
65.4%
70.1%
68.6%
64.6%
68.8%
67.9%
64.4%
Source: CEQ-IDB; Brazil (2009): Higgins and Pereira, 2013 CEQ-IDB
18
TABLE 10 – HORIZONTAL EQUITY OF TAXES AND TRANSFERS (GUATEMALA)
NON INDIGENOUS
INDIGENOUS
y<=1.25
% nat'l
36.32%
% nat'l
63.68%
1.25<y<=2.50
42.03%
57.97%
2.50<y<=4
52.64%
47.36%
y<=1.25
35.46%
64.54%
1.25<y<=2.50
42.30%
57.70%
2.50<y<=4
53.36%
46.64%
y<=1.25
23.80%
76.20%
1.25<y<=2.50
26.03%
73.97%
2.50<y<=4
35.04%
64.96%
y<=1.25
24.03%
75.97%
1.25<y<=2.50
23.54%
76.46%
2.50<y<=4
21.29%
78.71%
y<=1.25
19.68%
80.32%
1.25<y<=2.50
40.79%
59.21%
2.50<y<=4
68.90%
31.10%
y<=1.25
22.61%
77.39%
1.25<y<=2.50
49.93%
50.07%
2.50<y<=4
58.73%
41.27%
y<=1.25
51.13%
48.87%
1.25<y<=2.50
53.64%
46.36%
2.50<y<=4
63.17%
36.83%
y<=1.25
43.44%
56.56%
1.25<y<=2.50
44.53%
55.47%
2.50<y<=4
56.35%
43.65%
y<=1.25
41.92%
58.08%
1.25<y<=2.50
42.55%
57.45%
2.50<y<=4
54.69%
45.31%
Guatemala
POPULATION
MARKET INCOME
All Direct Transfers
CCT
Non-Contributory
Pensions
Other Direct
Transfers
Indirect Subsidies
Indirect Taxes
Net Indirect Taxes
Source: Guatemala (2010/2011): Cabrera and Moran, 2013 CEQ-IDB
19
TABLE 11 – HORIZONTAL EQUITY OF TAXES AND TRANSFERS (URUGUAY)
Uruguay
POPULATION
MARKET
INCOME
Direct Taxes
All Direct
Transfers
CCT
Noncontributory
pension
Other Direct
Transfers
Indirect Taxes
Net Indirect
Taxes
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
y<=1.25
1.25<y<=2.50
2.50<y<=4
WHITE
% nat'l
90.4%
90.5%
91.4%
90.2%
90.9%
91.7%
96.2%
85.3%
92.5%
89.7%
89.8%
90.7%
90.5%
90.0%
92.2%
90.6%
91.9%
89.2%
88.7%
88.4%
90.4%
89.3%
91.6%
92.1%
89.3%
91.6%
92.1%
AFRODESCENDANT
% nat'l
8.4%
8.3%
7.0%
8.3%
8.0%
6.8%
3.8%
14.0%
6.7%
8.9%
9.2%
7.8%
8.4%
8.8%
6.4%
9.0%
7.3%
9.6%
9.0%
10.6%
7.8%
9.2%
7.1%
6.3%
9.2%
7.1%
6.3%
INDIGENOUS
% nat'l
1.2%
1.2%
1.5%
1.5%
1.1%
1.4%
0.0%
0.7%
0.6%
1.4%
1.0%
1.5%
1.1%
1.2%
1.4%
0.4%
0.8%
1.1%
2.2%
1.0%
1.7%
1.5%
1.3%
1.4%
1.5%
1.3%
1.4%
Source: Uruguay (2009): Bucheli, Rossi and Amabile, 2013 CEQ-IDB.
20
FIGURE 1 – INCIDENCE OF DIRECT TRANSFERS BY MARKET INCOME CATEGORY AND ETHNO-RACIAL
GROUP (WITH RESPECT TO MARKET INCOME; IN PERCENT)
Brazil
Bolivia
200.0%
35.0%
Percent of Market Income
Percent of Market Income
40.0%
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
180.0%
160.0%
140.0%
120.0%
100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
0.0%
y < 1.25
1.25 < = y <
2.50
National
2.50 <= y <
4.00
4.00 <= y <
10.00
10.00 <= y <
50.00
y < 1.25
50.00 <= y
1.25 < = y < 2.50 <= y < 4.00 4.00 <= y <
2.50
10.00
National
Indigenous
White
Pardo
Black
Uruguay
Guatemala
14.0%
250.0%
12.0%
Percent of Market Income
Percent of Market Income
50.00 <= y
Income Group
Income Group
Non-Indigenous
10.00 <= y <
50.00
10.0%
8.0%
6.0%
4.0%
200.0%
150.0%
100.0%
50.0%
2.0%
0.0%
0.0%
y < 1.25
1.25 < = y <
2.50
National
2.50 <= y <
4.00
4.00 <= y <
10.00
Income Group
Non-Indigenous
10.00 <= y <
50.00
Indigenous
50.00 <= y
y < 1.25
1.25 < = y < 2.50
2.50 <= y < 4.00
4.00 <= y < 10.00 10.00 <= y < 50.00
Income Group
National
White
Afro-Descendant
Indigenous
Source: Author’s based on Bolivia (2009): Paz-Arauco et al., 2013 CEQ-IDB; Brazil (2009): Higgins and Pereira, 2013
CEQ-IDB; Guatemala (2010/2011): Cabrera and Moran, 2013 CEQ-IDB; Uruguay (2009): Bucheli, Rossi and Amabile,
2013 CEQ-IDB.
21
FIGURE 2 – GROWTH INCIDENCE CURVES OF POST-FISCAL INCOME BY MARKET INCOME
CATEGORY AND ETHNO-RACIAL GROUP (WITH RESPECT TO MARKET INCOME; IN PERCENT)
Bolivia
Brazil
Percent of Market Income
15.0%
12.5%
10.0%
7.5%
5.0%
2.5%
0.0%
-2.5%
-5.0%
-7.5%
y < 1.25
1.25 < = y <
2.50
2.50 <= y <
4.00
4.00 <= y <
10.00
10.00 <= y <
50.00
50.00 <= y
Percent change wrt to Market Income
17.5%
140.0%
120.0%
100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
-20.0%
-40.0%
y < 1.25
1.25 < = y <
2.50
Non-Indigenous
National
Indigenous
10.00 <= y <
50.00
50.00 <= y
White
Pardo
Black
Uruguay
Guatemala
250.0%
10.0%
8.0%
Percent of Market Income
Percent Change wrt Market Income
4.00 <= y <
10.00
Income Group
Income Group
National
2.50 <= y <
4.00
6.0%
4.0%
2.0%
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
200.0%
150.0%
100.0%
50.0%
0.0%
-50.0%
-10.0%
y < 1.25
1.25 < = y <
2.50
2.50 <= y < 4.00
4.00 <= y <
10.00
10.00 <= y <
50.00
Income Group
National
Non-Indigenous
Indigenous
50.00 <= y
y < 1.25
1.25 < = y <
2.50
2.50 <= y <
4.00
4.00 <= y <
10.00
10.00 <= y <
50.00
50.00 <= y
Income Group
National
White
Afro-Descendant
Indigenous
Source: Author’s based on Bolivia (2009): Paz-Arauco et al., 2013 CEQ-IDB; Brazil (2009): Higgins and Pereira, 2013
CEQ-IDB; Guatemala (2010/2011): Cabrera and Moran, 2013 CEQ-IDB; Uruguay (2009): Bucheli, Rossi and Amabile,
2013 CEQ-IDB.
22
APPENDIX
The incidence of social spending and taxes: methodological highlights23
Let’s define the before taxes and transfers income of unit h as Ih and net taxes of type i as Ti. Let’s define the
“allocator” of tax i to unit h as Sih (or the share of net tax i borne by unit h). Then, post-tax income of unit h
can be defined as: Yh = Ih - ∑i TiSih. This method is known in the literature as the “accounting approach.” 24
The CEQ handbook by Lustig and Higgins (2013) presents a detailed description of this methodology.
The exercise applied here is quite comprehensive because the studies cover a very broad spectrum of taxes
and transfers. In particular, the taxes examined in these studies include direct (personal income tax and
contributions to social security) and indirect consumption taxes. Spending covers transfers and indirect
subsidies, in addition to in-kind transfers from public spending on education and health. 25 The full description
of the tax and transfers system in Bolivia, Brazil and Uruguay can be found in Lustig et al. (2013). For
Guatemala, see Cabrera et al. (2014).
The incidence analysis used here is point-in-time and calculates the average incidence. By definition, the
accounting approach does not incorporate behavioral or general equilibrium effects. However, the analysis is
not mechanical: the incidence of taxes is calculated based on their (assumed) economic rather than statutory
incidence. For instance, individual income taxes and social security contributions (both by employee and
employer) are assumed to be paid by formal sector labor only, and consumption taxes are fully shifted forward
to consumers. In the case of consumption taxes, the analyses take into account the lower incidence associated
with auto-consumption (in rural areas) and informality.
i. Income Concepts Before and After Fiscal Policy
Defining and constructing the income concepts that prevail before and after taxes and transfers is the
cornerstone of incidence analysis. It entails the process by which taxes, subsidies and transfers are allocated
to each household to assess how incomes, and thus, inequality and poverty indicators, change with fiscal
policy. Income concepts are essential to analyze the incidence of specific fiscal interventions and whether they
are progressive or regressive.
This section draws heavily from Lustig et al. (2013). Note that although some of the text is the same as in the latter, quotation
marks were not used for ease of exposition.
24 For a description, applications and limitations of the accounting approach see, for example, Adema and Ladaique (2005), Alleyne
et al. (2004), Atkinson (1983), Bergh (2005), Bourguignon and Pereira da Silva (2003), Barr (2004), Barros et al. (2009), Birdsall et
al. (2008), Breceda et al. (2008), Dilnot et al. (1990), Ferreira and Robalino (2010), Fiszbein et al. (2009), Grosh et al. (2008), Goñi
et al. (2011), Kakwani (1977), Lambert (2002), Lora (2006), Morra et al. (2009), Martinez-Vazquez (2008), Moreno-Dodson and
Wodon (2008), O’Donnell et al. (2008), Shah (2003), Suits (1977), van de Walle and Nead (1995), World Bank (2000/2001, 2006,
2009, 2011).
25 The studies exclude corporate and international trade taxes, some spending categories (such as infrastructure investments
including urban services and rural roads that benefit the poor), and other public goods.
23
23
The studies included here use five income concepts: market, net market, disposable, post-fiscal, and final
income.26 Market income27 is total current income before direct taxes, 28 equal to the sum of gross (pre-tax) wages
and salaries in the formal and informal sectors (also known as earned income), income from capital (dividends,
interest, profits, rents, etc.) in the formal and informal sectors (excludes capital gains and gifts), autoconsumption (except in the case of Bolivia),29 imputed rent for owner-occupied housing, private transfers
(remittances and other private transfers such as alimony), and old-age and other pensions from the
contributory social security system. Net market income equals market income minus direct personal income
taxes on all income sources (included in market income) that are subject to taxation and all contributions to
social security except for the portion going towards pensions. 30 Disposable income is equal to the sum of net
market income plus direct government transfers (mainly cash transfers but can include food transfers where
applicable). Post-fiscal income is defined as disposable income plus indirect subsidies minus indirect taxes (e.g.,
value added tax, sales tax, etc.). Final income is defined as post-fiscal income plus government in-kind transfers
in the form of free or subsidized services in education, health, and housing minus co-payments or user fees.31
These income concept definitions are summarized in Diagram 1. The results presented in the main text do
not include inequality measures for Final Income. They are included in the Statistical Appendix, available upon
request.
For more details on concepts and definitions, see Lustig and Higgins (2013).
Market income is sometimes called primary income.
28 Taxes include non-pension social security contributions in the benchmark analysis and all social security contributions in the
sensitivity analysis.
29 In the case of Bolivia the results with autoconsumption are specious (e.g., Bolivia ends up with the same distribution of income
as Uruguay and a lower rural poverty than Mexico) so we opted to not use them.
30 Since here we are treating contributory pensions as part of market income, the portion of the contributions to social security
going towards pensions is treated as “saving.”
31 One may also include participation costs such as transportation costs or foregone incomes because of use of time in obtaining
benefits. In our study, they were not included.
26
27
24
DIAGRAM 1 – DEFINITIONS OF INCOME CONCEPTS: A STYLIZED PRESENTATION
In the fiscal incidence literature, pensions from contributory systems have been sometimes treated as part of
market income and other times as government transfers. Arguments exist both for treating contributory
pensions as part of market income because they are deferred income (Breceda, Rigolini, and Saavedra, 2008;
Immervoll et al., 2009) and for treating them as a government transfer, especially in systems with a large
subsidized component (Goñi, López, and Servén, 2011; Immervoll et al., 2009; Lindert, Skoufias, and Shapiro,
2006; Silveira et al., 2011). Since this is an unresolved issue, in our study we defined a benchmark case in which
contributory pensions are part of market income. We also performed a sensitivity analysis where pensions are
classified under government transfers.32 The principal results presented here are for the benchmark analysis.
32
Immervoll et al. (2009) do the analysis under these two scenarios as well.
25
An analysis of the effects of treating pensions as transfers is included at the end of this overview. More detailed
results of this sensitivity analysis can be found in the Statistical Appendix, available upon request.
ii. Tax Shifting Assumptions
Consistent with other conventional tax incidence analyses, here we assume that the economic burden of direct
personal income taxes is borne by the recipient of income. The burden of payroll and social security taxes is
assumed to fall entirely on workers. Consumption taxes are assumed to be shifted forward to consumers.
These assumptions are strong because, in essence, they imply that labor supply is perfectly inelastic and
consumers have perfectly inelastic demands for goods and services. In practice they provide a reasonable
approximation.33
Evasion of direct income and payroll taxes is taken into account in the analysis by assuming that individuals
who do not participate in the contributory social security system do not pay income or payroll taxes (Brazil’s
survey includes a question on tax payments so tax evasion is assumed to be as reported in the survey). In the
case of direct taxes (simulated using 2010 tax legislation), it was assumed that workers and self-employed in
firms with less than 5 employees did not pay this tax. In the case of indirect (consumption) taxes, assumptions
to take evasion into account varied. In Bolivia, it was assumed that purchases in informal sector establishments
avoid indirect taxes both in urban and rural areas, but the rest of rural purchases include indirect taxes. In
Brazil, the indirect tax rate for each type of good or service was obtained from a secondary source that
estimated the effective rates taking into account evasion and indirect effects, but not the distribution of this
evasion (Siqueira et al, 2010). In Guatemala, unprocessed food in rural areas was assumed not to pay VAT;
purchases in urban informal markets were also assumed not to pay VAT.34 In Uruguay, the legal rate of VAT
was applied to every purchase regardless of place of purchases or region because evasion of such taxes is very
small. Thus for Brazil and Uruguay, this analysis may thus overestimate the impact and regressivity of indirect
taxes. Taking into account informality in this analysis may also be more important in some countries than in
others, depending on the actual extent of informality. Care must be taken in comparing the results for postfiscal incomes.
iii. Incidence of Public Services
The approach to estimate the incidence of public spending on education and health followed here is the socalled “benefit or expenditure incidence” or “government cost” approach. In essence, we use per beneficiary
input costs obtained from administrative data as the measure of average benefits. This approach, also known
as the “classic” or “non-behavioral approach,” amounts to asking the following question: how much would
the income of a household have to be increased if it had to pay for the free or subsidized public service at full
cost? The paper presents the results for coverage of tertiary education by income category and ethnic- racial
groups only. The whole array of results for education and health are in the Statistical Appendix, available upon
request.
For example, Martinez-Vazquez (2008, p. 123) finds that “…the results obtained with more realistic and laborious assumptions
on elasticities tend to yield quite similar results.”
34 For more details, see Cabrera et al. (2014).
33
26
iv. Allocating Taxes and Transfers at the Household Level
Information on direct and indirect taxes, transfers in cash and in-kind, and subsidies cannot always be obtained
directly from household surveys. When it can be obtained, we call this the direct identification method. When the
direct method is not feasible, one can use the inference, simulation, imputation methods, or an alternate
source. As a last resort, one can use secondary sources.35 The specific method used for each category of taxes
and transfers in each country study is available upon request. The direct identification method was the method
most frequently used, especially for cash transfers. Direct personal income taxes and indirect consumption
taxes were simulated (including assumptions for evasion) in all cases except for direct taxes in Brazil. As
discussed above, in-kind transfers were imputed using the government cost approach.
35
The methods one can use to allocate taxes and transfers are described in detail in Lustig and Higgins (2013).
27
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CEQ WORKING PAPER SERIES
WORKING PAPER NO. 1
Lustig, Nora and Sean Higgins. 2013. Commitment to Equity Assessment (CEQ): Estimating the Incidence of Social Spending,
Subsidies and Taxes. Handbook. CEQ Working Paper No. 1, Center for Inter-American Policy and Research and
Department of Economics, Tulane University and Inter-American Dialogue, September.
WORKING PAPER NO. 2
Lustig, Nora. 2013. Commitment to Equity: Diagnostic Questionnaire. CEQ Working Paper No. 2, Center for InterAmerican Policy and Research and Department of Economics, Tulane University and Inter-American Dialogue,
January.
WORKING PAPER NO. 3
Lustig, Nora and George Gray Molina, Sean Higgins, Miguel Jaramillo, Wilson Jiménez, Veronica Paz, Claudiney
Pereira, Carola Pessino, John Scott, and Ernesto Yañez. 2012. The Impact of Taxes and Social Spending on Inequality
and Poverty in Argentina, Bolivia,Brazil, Mexico and Peru: A Synthesis of Results. CEQ Working Paper No. 3, Center for
Inter-American Policy and Research and Department of Economics, Tulane University and Inter-American
Dialogue, August.
WORKING PAPER NO. 4
Lustig, Nora and Sean Higgins. 2013. Fiscal Incidence, Fiscal Mobility and the Poor: A New Approach. CEQ Working Paper
No. 4, Center for Inter-American Policy and Research and Department of Economics, Tulane University and
Inter-American Dialogue, January.
WORKING PAPER NO. 5
Lustig, Nora and Carola Pessino. 2013. Social Spending and Income Redistribution in Argentina in the 2000s: the Rising Role of
Noncontributory Pensions. CEQ Working Paper No. 5, Center for Inter-American Policy and Research and
Department of Economics, Tulane University and Inter-American Dialogue, REVISED August 2013.
WORKING PAPER NO. 6
Paz Arauco, Verónica, George Gray Molina, Wilson Jiménez Pozo, and Ernesto Yáñez Aguilar. 2013. Explaining Low
Redistributive Impact in Bolivia. CEQ Working Paper No. 6, Center for Inter-American Policy and Research and
Department of Economics, Tulane University and Inter-American Dialogue, REVISED April.
WORKING PAPER NO. 7
Higgins, Sean and Claudiney Pereira. 2013. The Effects of Brazil’s High Taxation and Social Spending on the Distribution of
Household Income. CEQ Working Paper No. 7, Center for Inter-American Policy and Research and Department of
Economics, Tulane University and Inter-American Dialogue, REVISED May 2013.
WORKING PAPER NO. 8
Scott, John. 2013. Redistributive Impact and Efficiency of Mexico’s Fiscal System. CEQ Working Paper No. 8, Center for
Inter-American Policy and Research and Department of Economics, Tulane University and Inter-American
Dialogue, REVISED July 2013.
WORKING PAPER NO. 9
Jaramillo Baanante, Miguel. 2013. The Incidence of Social Spending and Taxes in Peru. CEQ Working Paper No. 9, Center
for Inter-American Policy and Research and Department of Economics, Tulane University and Inter-American
Dialogue, REVISED May 2013.
WORKING PAPER NO. 10
Bucheli, Marisa and Nora Lustig, Máximo Rossi and Florencia Amábile. 2013. Social Spending, Taxes, and Income
Redistribution in Uruguay. CEQ Working Paper No. 10, Center for Inter-American Policy and Research and
Department of Economics, Tulane University and Inter-American Dialogue, REVISED July 2013.
WORKING PAPER NO. 11
Higgins, Sean and Nora Lustig, Julio Ramirez and Billy Swanson. 2013. Social Spending, Taxes and Income Redistribution in
Paraguay. CEQ Working Paper No. 11, Center for Inter-American Policy and Research and Department of
Economics, Tulane University and Inter-American Dialogue, November.
WORKING PAPER NO. 12
Alvaredo, Facundo and Juliana Londoño Vélez. 2013. High Incomes and Personal Taxation in a Developing Economy:
Colombia 1993-2010. CEQ Working Paper No. 12, Center for Inter-American Policy and Research and
Department of Economics, Tulane University and Inter-American Dialogue, March.
WORKING PAPER NO. 13 English
Lustig, Nora, and Carola Pessino and John Scott. 2013. The Impact of Taxes and Social Spending on Inequality and Poverty in
Argentina, Bolivia, Brazil, Mexico, Peru and Uruguay: An Overview. CEQ Working Paper No. 13, Center for Inter-American
Policy and Research and Department of Economics, Tulane University and Inter-American Dialogue, REVISED
August 2013.
WORKING PAPER NO. 13 Spanish
Lustig, Nora, and Carola Pessino and John Scott. 2014. El Impacto del Sistema Tributario y del Gasto Social sobre la
Desigualdad y la Pobreza en Argentina, Bolivia, Brasil, México, Perú y Uruguay: un Panorama General. CEQ Working Paper
No. 13, Center for Inter-American Policy and Research and Department of Economics, Tulane University and
Inter-American Dialogue, October 2014.
WORKING PAPER NO. 14
Higgins, Sean and Nora Lustig. 2013. Measuring Impoverishment: An Overlooked Dimension of Fiscal Incidence. CEQ Working
Paper No. 14, Center for Inter-American Policy and Research and Department of Economics, Tulane University
and Inter-American Dialogue, April.
WORKING PAPER NO. 15
Tanzi, Vito. 2013. Tax Reform in Latin America: A long term assessment. CEQ Working Paper No. 15, Center for InterAmerican Policy and Research and Department of Economics, Tulane University and Inter-American Dialogue,
April.
WORKING PAPER NO. 16
Higgins, Sean, Nora Lustig, Whitney Ruble and Tim Smeeding. 2013. Comparing the Incidence of Taxes and Social Spending
in Brazil and the United States. CEQ Working Paper No. 16, Center for Inter-American Policy and Research and
Department of Economics, Tulane University and Inter-American Dialogue, November.
WORKING PAPER NO. 17
López-Calva, Luis F., Nora Lustig, John Scott y Andrés Castañeda. 2014. Gasto social, redistribución del ingreso y reducción
de la pobreza en México: evolución y comparación con Argentina, Brasil y Uruguay. CEQ Working Paper No. 17, Center for
Inter-American Policy and Research and Department of Economics, Tulane University and Inter-American
Dialogue, REVISED March.
WORKING PAPER NO. 18 Spanish
Sauma, Juan Diego Trejos. 2014. Gasto público social, impuestos, redistribución del ingreso y pobreza en Costa Rica. CEQ
Working Paper No. 18, Center for Inter-American Policy and Research and Department of Economics, Tulane
University and Inter-American Dialogue, January.
WORKING PAPER NO. 18 English
Sauma, Juan Diego Trejos. 2014. Social Public Spending, Taxes, Redistribution of Income, and Poverty in Costa. CEQ Working
Paper No. 18, Center for Inter-American Policy and Research and Department of Economics, Tulane University
and Inter-American Dialogue, January.
WORKING PAPER NO. 19
Bucheli, Marisa, Máximo Rossi and Florencia Amábile. 2015. Inequality and Poverty in Uruguay by Race: The Impact of Fiscal
Policies. CEQ Working Paper No. 19, Center for Inter-American Policy and Research and Department of
Economics, Tulane University and Inter-American Dialogue, REVISED February.
WORKING PAPER NO. 20
Cabrera, Maynor, Nora Lustig and Hilcías Morán. 2014. Fiscal Policy, Inequality and the Ethnic Divide in Guatemala. CEQ
Working Paper No. 20, Center for Inter-American Policy and Research and Department of Economics, Tulane
University and Inter-American Dialogue, October.
WORKING PAPER NO. 21
Burdín, Gabriel, Fernando Esponda, and Andrea Vigorito. 2014. Inequality and top incomes in Uruguay: a comparison between
household surveys and income tax micro-data. CEQ Working Paper No. 21, Center for Inter-American Policy and
Research and Department of Economics, Tulane University and Inter-American Dialogue, May.
WORKING PAPER NO. 22
Lustig, Nora. 2015. Fiscal policy and ethno-racial inequality in Bolivia, Brazil, Guatemala and Uruguay. CEQ Working Paper
No. 22, Center for Inter-American Policy and Research and Department of Economics, Tulane University and
Inter-American Dialogue, January.
WORKING PAPER NO. 23
Lustig, Nora. 2015. Taxes, Transfers, Inequality and the Poor in the Developing World. Round 1. CEQ Working Paper No. 23,
Center for Inter-American Policy and Research and Department of Economics, Tulane University and InterAmerican Dialogue. Forthcoming.
WORKING PAPER NO. 24
Lustig, Nora and Marcela Melendez. 2015. The Impact of Taxes and Transfers on Inequality and Poverty in Colombia. CEQ
Working Paper No 24, Center for Inter-American Policy and Research and Department of Economics, Tulane
University and Inter-American Dialogue. Forthcoming.
WORKING PAPER NO. 25
Lustig Nora, Rodrigo Aranda and Ali Enami. 2015. Measuring the Contribution of Taxes and Transfers to Changes in Inequality
and Poverty: How to Address Path Dependency. CEQ Working Paper No. 25, Center for Inter-American Policy and
Research and Department of Economics, Tulane University and Inter-American Dialogue. Forthcoming.
WORKING PAPER NO. 26
Beneke, Margarita, Nora Lustig y José Andrés Oliva. 2015. El impacto de los impuestos y el gasto social en la desigualdad y la
pobreza en El Salvador. CEQ Working Paper No. 26, Center for Inter-American Policy and Research and
Department of Economics, Tulane University and Inter-American Dialogue, February.
WORKING PAPER NO. 27
Bucheli, Marisa. 2015. Public transfers and poverty reduction: an evaluation of program contribution to the exit rate from poverty of
children and the elderly. CEQ Working Paper No. 27, Center for Inter-American Policy and Research and
Department of Economics, Tulane University and Inter-American Dialogue, January.
WORKING PAPER NO. 28
Llerena Pinto, Freddy Paul, María Christina Llerena Pinto, Roberto Carlos Saá Daza, María Andrea
Llerena Pinto. 2015. Social Spending, Taxes and Income Redistribution in Ecuador. CEQ Working Paper No. 28, Center
for Inter-American Policy and Research and Department of Economics, Tulane University and Inter-American
Dialogue, February.
WORKING PAPER NO. 29
Inchauste, Gabriela, Nora Lustig, Mashekwa Maboshe, Catriona Purfield, and Ingrid Woolard. 2015.
The Distributional Impact of Fiscal Policy in South Africa. CEQ Working Paper No. 29, Center for
Inter-American Policy and Research and Department of Economics, Tulane University and Inter-American
Dialogue, Forthcoming.
http://www.commitmentoequity.org
WHAT IS CEQ?
Led by Nora Lustig since 2008, the Commitment to Equity (CEQ)
project is an initiative of the Center for Inter-American Policy
and Research (CIPR) and the Department of Economics, Tulane
University, the Center for Global Development and the Inter-American
Dialogue. The project’s main output is the CEQ Assessment, a
methodological framework designed to analyze the impact of
taxation and social spending on inequality and poverty in individual
countries. The main objective of the CEQ is to provide a roadmap
for governments, multilateral institutions, and nongovernmental
organizations in their efforts to build more equitable societies.
Tulane University’s Center for Inter-American Policy and Research,
the School of Liberal Arts and the Stone Center for Latin American
Studies as well as the Bill & Melinda Gates Foundation, the Canadian
International Development Agency (CIDA), the Development Bank
of Latin America (CAF), the General Electric Foundation, the InterAmerican Development Bank (IADB), the International Fund for
Agricultural Development (IFAD), the Norwegian Ministry of Foreign
Affairs, OECD, the United Nations Development Programme’s
Regional Bureau for Latin America and the Caribbean (UNDP/
RBLAC), and the World Bank.
www.commitmentoequity.org
The CEQ logo is a stylized graphical representation of a
Lorenz curve for a fairly unequal distribution of income (the
bottom part of the C, below the diagonal) and a concentration
curve for a very progressive transfer (the top part of the C).